ADVERSARIAL RESILIENCE AND OPERATIONAL THREAT MODELS FOR LARGE LANGUAGE MODELS: A COMPREHENSIVE FRAMEWORK FOR EVALUATION, RED-TEAMING, AND AUTOMATED DEFENSE

John A. Mercer

Authors

John A. Mercer Department of Computer Science, Universitas Airlangga, Indonesia

Keywords:

Adversarial robustness, large language models, red-teaming

Abstract

This article presents a comprehensive, publication-ready exposition of adversarial resilience for large language models (LLMs), synthesizing practical toolsets, theoretical foundations, benchmark methodologies, and operational threat modeling to create an end-to-end framework for evaluation and mitigation. The work integrates knowledge from open-source adversarial toolkits, empirical jailbreaking studies, red-teaming methodologies, and adversarial machine learning theory to propose a rigorous, reproducible, and operationalizable pipeline for assessing and improving LLM security. The abstracted framework addresses (1) taxonomy and attack surfaces for instruction-tuned LLMs, (2) benchmarking and automated testing using contemporary toolchains, (3) metrics and evaluation protocols that balance safety and utility, and (4) a layered defense strategy that combines data hygiene, model-level interventions, and runtime monitoring. Key contributions include a mapping between attack techniques (prompt leakage, persona-based jailbreaks, universal triggers) and defensive controls; an extensible evaluation methodology using adversarial benchmarking tools and red-team automation; and a set of operational recommendations for integrating continuous adversarial testing into LLM development lifecycles. The article situates these contributions within the extant literature on adversarial examples, prompting methods, and red-teaming, and discusses policy and deployment implications for practitioners.

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